Screening of ionic liquids as green entrainers for ethanol water separation by extractive distillation: COSMO-RS prediction and aspen plus simulation.

Ionic liquids (ILs) have been demonstrated as promising alternatives to conventional entrainers in separation of azeotropic mixtures mostly investigating phase equilibrium and process design scenarios. However, proper selection of ILs for a specific task always remains challenging. Hence a simulation tool, i.e. conductor like screening model for real solvents (COSMO-RS) was applied to address this challenge. Furthermore, screened ILs were simulated as entrainers for ethanol water separation by extractive distillation. The current study also aims to demonstrate a systematic approach to retrofit existing processes, by employing ILs as green entrainers. Screening of twenty-five (25) ILs was carried out using COSMO-RS to select suitable ILs as green entrainers based on activity coefficient, capacity and selectivity. Results illustrated that tetramethylammonium chloride ([TMAm][Cl]) due to its strong hydrogen bonding ability was found to be the best ILs entrainer. Moreover, in order to reduce the operating costs without compromising desired product purity (ethanol purity ≥99.5% in top product), the selected ILs (8 kg/h) in a mixture with ethylene glycol (72 kg/h) were simulated using Aspen plus v.11. The simulation results revealed that by combining tetramethylammonium chloride (2 kg/h) with ethylene glycol (78 kg/h) reduced 7.26 tons of CO2 emissions/year through heat integration by saving 1.49*108 kJ/year energy besides minimizing operating costs. In conclusion, the systematic selection of ILs as green entrainers in combination with ethylene glycol and then the appropriate simulation of the whole system will ultimately reduce the cost of the separation process and reduce the emission of greenhouse gases as well utilization of toxic conventional entrainers.

Hepatocellular Carcinoma Due to Nonalcoholic Fatty Liver Disease: Current Concepts and Future Challenges.

Obesity has been labeled as the global pandemic of the 21st century, resulting from a sedentary lifestyle and caloric excess. Nonalcoholic fatty liver disease (NAFLD), characterized by excessive hepatic steatosis, is strongly associated with obesity and metabolic syndrome and is estimated to be present in one-quarter of the world population, making it the most common cause of the chronic liver disease (CLD). NAFLD spectrum varies from simple steatosis to nonalcoholic steatohepatitis (NASH) and cirrhosis. The burden of NAFLD has been predicted to increase in the coming decades resulting in increased rates of decompensated cirrhosis, hepatocellular carcinoma (HCC), and liver-related deaths. In the current review, we describe the pathophysiology of NAFLD and NASH, risk factors associated with disease progression, related complications, and mortality. Later, we have discussed the changing epidemiology of HCC, with NAFLD emerging as the most common cause of CLD and HCC. We have also addressed the risk factors of HCC development in the NAFLD population (including demographic, metabolic, genetic, dietary, and lifestyle factors), presentation of NAFLD-associated HCC, its prognosis, and the issue of HCC development in non-cirrhotic NAFLD. Lastly, the problems related to HCC screening in the NAFLD population, the remaining challenges, and future directions, especially the need to identify the high-risk individuals, will be discussed. We will conclude the review by summarizing the clinical evidence for treating fibrosis and preventing HCC in those at risk with NAFLD-associated HCC.

A Comprehensive Performance Analysis of Transfer Learning Optimization in Visual Field Defect Classification.

Numerous research have demonstrated that Convolutional Neural Network (CNN) models are capable of classifying visual field (VF) defects with great accuracy. In this study, we evaluated the performance of different pre-trained models (VGG-Net, MobileNet, ResNet, and DenseNet) in classifying VF defects and produced a comprehensive comparative analysis to compare the performance of different CNN models before and after hyperparameter tuning and fine-tuning. Using 32 batch sizes, 50 epochs, and ADAM as the optimizer to optimize weight, bias, and learning rate, VGG-16 obtained the highest accuracy of 97.63 percent, according to experimental findings. Subsequently, Bayesian optimization was utilized to execute automated hyperparameter tuning and automated fine-tuning layers of the pre-trained models to determine the optimal hyperparameter and fine-tuning layer for classifying many VF defect with the highest accuracy. We found that the combination of different hyperparameters and fine-tuning of the pre-trained models significantly impact the performance of deep learning models for this classification task. In addition, we also discovered that the automated selection of optimal hyperparameters and fine-tuning by Bayesian has significantly enhanced the performance of the pre-trained models. The results observed the best performance for the DenseNet-121 model with a validation accuracy of 98.46% and a test accuracy of 99.57% for the tested datasets.

Entropy in Education System: Transformation of an Individual Through Meaningful Interactions in a Community of Inquiry.

Every individual is unique and may serve a unique purpose in this life. Education is widely accepted to be the means of transformation of individuals so that they may achieve their unique success or create their own lives. However, not every individual seems to be realizing their true potential. This paper explores the concept of entropy in education system as a force that is usually imagined to oppose realization of potential of an individual during life in this phenomenal world. Alternatively, the same may provide an impetus that is necessary to bring in organization in oneself to realize the hidden potential. A one group Pretest-Posttest quasi-experimental design was used to draw the conclusions on data obtained from participants of workshops in three different modes, viz. face-to-face Pre COVID-19, face-to-face in COVID-19 with SOPs, and online in COVID-19. Realization of an individual's potential was represented as a dependent variable, i.e. transformation in cognition, skills, and attitude while the independent variables taken into account were the meaningful interactions of an individual with peers and advanced learners in a designed environment. It was inferred from the results that transformation in learners' cognition (6-30 %), skills (0-20 %), and attitude (5-32 %) occurred through human discourse, in a community of inquiry.

Sustainable Use of Marble Waste in Industrial Production of Fired Clay Bricks and Its Employment for Treatment of Flue Gases.

In the current study, waste marble powder was utilized to produce lightweight fired clay bricks and in the treatment of flue gases. Marble waste powder used in clay bricks and flue gas treatment was collected from local marble processing plants, and its chemical composition was analyzed using energy-dispersive X-ray analysis and X-ray fluorescence. Clay bricks were produced in local fired clay brick kiln by partial replacement (10-40%) of clay with calcined and uncalcined waste marble powder. The temperature in the firing zone of the kiln was 1000-1100 °C. Clay brick properties such as compressive strength, bulk density, porosity, and water absorption were analyzed. Results show that the porosity of clay bricks increases with an increase in marble waste percentage, and due to an increase in porosity, water absorption of clay bricks also increases, while compressive strength and bulk density decrease. Clay bricks with 10% uncalcined marble waste and 20% calcined marble waste satisfy the required Turkish and European compressive strength standard. Marble waste decreases the concentration of sulfur dioxide and particulate matter in flue gases. There is up to 43 and 65% reduction in the concentration of sulfur dioxide and particulate matters, respectively, in flue gases, after treatment with marble waste. The addition of marble waste to clay bricks is an effective and economical solution for waste marble powder management. Furthermore, it can be used for the treatment of flue gases from brick kilns before its addition to clay bricks.

Are first-year healthcare undergraduates at an Asian university ready for interprofessional education?

The health professions education at the National University of Singapore (NUS) is addressing the change in health service delivery in Singapore through the provision of interprofessional education (IPE). The success of this educational approach depends on the readiness of health profession students to learn together. Although such readiness has been explored in the Western institutions, little is known among healthcare students in Asian university. This study aimed to examine the readiness of healthcare students' toward IPE at NUS. The Readiness for Interprofessional Learning Scale was administered to first-year medical, nursing, pharmacy and dentistry students. Overall, the students' readiness for IPE was high. However, compared to the medical students, a significant less readiness was reported by the pharmacy and dentistry students. This study advocates the need to enhance the pharmacy and dentistry students' awareness of their professional role on collaborative practice and the benefits of IPE to their clinical practice.

Knowledge, attitudes and practices survey on organ donation among a selected adult population of Pakistan.

BACKGROUND: To determine the knowledge, attitudes and practices regarding organ donation in a selected adult population in Pakistan. METHODS: Convenience sampling was used to generate a sample of 440; 408 interviews were successfully completed and used for analysis. Data collection was carried out via a face to face interview based on a pre-tested questionnaire in selected public areas of Karachi, Pakistan. Data was analyzed using SPSS v.15 and associations were tested using the Pearson's Chi square test. Multiple logistic regression was used to find independent predictors of knowledge status and motivation of organ donation. RESULTS: Knowledge about organ donation was significantly associated with education (p = 0.000) and socioeconomic status (p = 0.038). 70/198 (35.3%) people expressed a high motivation to donate. Allowance of organ donation in religion was significantly associated with the motivation to donate (p = 0.000). Multiple logistic regression analysis revealed that higher level of education and higher socioeconomic status were significant (p < 0.05) independent predictors of knowledge status of organ donation. For motivation, multiple logistic regression revealed that higher socioeconomic status, adequate knowledge score and belief that organ donation is allowed in religion were significant (p < 0.05) independent predictors. Television emerged as the major source of information. Only 3.5% had themselves donated an organ; with only one person being an actual kidney donor. CONCLUSION: Better knowledge may ultimately translate into the act of donation. Effective measures should be taken to educate people with relevant information with the involvement of media, doctors and religious scholars.